Skip to main content
SpringerLink
Log in

Subspace projection semi-real-valued MVDR algorithm based on vector sensors array processing

  • Brain- Inspired computing and Machine learning for Brain Health
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The existing SRV-MVDR (semi-real-valued MVDR) algorithm is only applicable to the pressure sensors array and cannot distinguish between mirror radiation sources and real source (DOA fuzzy), and this paper presents a method of vector sensors array SRV-MVDR based on subspace projection. Compared with the existing SRV-MVDR, only half spectrum search is needed to solve the DOA fuzzy problem. No subsequent discrimination is needed. The data collected by vector sensors array are processed jointly by using the idea of dimensionality reduction so that it satisfies the processing condition of SRV-MVDR method. Theoretical analysis and computer simulation show that this method has robustness. At the same time, it is more suitable for low SNR and small snapshots and has broad prospects in practical engineering.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Schmidt R (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34(3):276–280

    Article  Google Scholar 

  2. Liu GH, Chen H, Sun XY et al (2016) Modified MUSIC algorithm for DOA estimation with nyström approximation. IEEE Sens J 16(12):4673–4674

    Article  Google Scholar 

  3. Roy R, Paulraj A, Kailath T (1986) ESPRIT—a subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans Acoust Speech Signal Process 34(5):1340–1342

    Article  Google Scholar 

  4. Steinwandt Jens, Roemer Florian, Haardt Martin (2017) Generalized least squares for ESPRIT-type direction of arrival estimation. IEEE Signal Process Lett 24(11):1681–1685

    Article  Google Scholar 

  5. Li Jianfeng, Jiang Defu, Zhang Xiaofei (2017) DOA estimation based on combined unitary ESPRIT for coprime MIMO radar. IEEE Commun Lett 21(1):96–99

    Article  Google Scholar 

  6. Sørensen Mikael, De Lathauwer Lieven (2016) Multiple invariance ESPRIT for nonuniform linear arrays: a coupled canonical polyadic decomposition approach. IEEE Trans Signal Process 64(14):3693–3704

    Article  MathSciNet  Google Scholar 

  7. Wax M, Kailath T (1985) Detection of signals by information theoretic criteria. IEEE Trans Acoust Speech Signal Process 33(2):387–392

    Article  MathSciNet  Google Scholar 

  8. Wu HT, Yang J, Chen FK (1995) Source number estimation using transformed Gerschgorin radii. IEEE Trans Signal Process 43(6):1325–1333

    Article  Google Scholar 

  9. Cao JW, Lai X P(2016). MVDR beamformer analysis of acoustic vector sensor with single directional interference. In: IEEE international symposium on circuits and systems (ISCAS), pp 774–777

  10. Capon J (1969) High-resolution frequency wavenumber spectrum analysis. Proc IEEE 57(8):1408–1418

    Article  Google Scholar 

  11. Yan Feng-Gang, Liu Shuai, Wang Jun et al (2018) Unitary direction of arrival estimation based on a second forward/backward averaging technique. IEEE Commun Lett 22(3):554–557

    Article  Google Scholar 

  12. Wang B, Chen F, Chen Y et al (2017) Energy DOA estimation of MUSIC symmetrical compressed spectrum on vector sensors array. Clust Comput 2017(10):1–8

    Google Scholar 

  13. Cao XH, Liu HW, Wu SJ (2008) DOA estimation based on online music algorithm. J Electron Inf Technol 30(11):2658–2661

    Article  Google Scholar 

  14. Huarng KC, Yeh CC (1991) A unitary transformation method for angle-of-arrival estimation. IEEE Trans Signal Process 39(4):975–977

    Article  Google Scholar 

  15. Wu JX, Wang T, Suo ZY et al (2009) DOA estimation for ULA by spectral Capon rooting method. Electron Lett 45(1):84–85

    Article  Google Scholar 

  16. Yan FG, Wang J, Shen Y et al (2015) Efficient direction-of-arrival estimation based on semi-real-valued Capon. J Electron Inf Technol 37(04):811–816

    Google Scholar 

  17. Zhang W, Shang L (2017) Method of direction of arrival tracking for multiple targets under water with single vector hydrophone. J Natl Univ Def Technol 39(02):114–119

    Google Scholar 

  18. Cao Jiuwen, Liu Jun, Wang Jianzhong et al (2017) Acoustic vector sensor: reviews and future perspectives. IET Signal Process 11(1):1–9

    Article  Google Scholar 

  19. Yun Yu, Ling Qing, Jiang Xu (2015) Pressure and velocity cross-spectrum of normal modes in low-frequency acoustic vector field of shallow water and its application. J Syst Eng Electron 26(2):241–249

    Article  Google Scholar 

  20. Zhang L, Yao X, Yang D (2013) Feature extraction based on vector hydrophone in the waveguide environment. In: 2013 OCEANS-San Diego, pp 1–8

  21. Tam Ping Kwan, Wong Kainam Thomas, Song Yang (2014) An hybrid Cramér-Rao bound in closed form for direction-of-arrival estimation by an “acoustic vector sensor” with gain-phase uncertainties. IEEE Trans Signal Process 62(10):2504–2516

    Article  MathSciNet  Google Scholar 

  22. Golub GH, Van Loan CH (1996) Matrix computations. The Johns Hopkins University Press, Baltimore, MD, pp 70–74

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation (11574120, U1636117); the Natural Science Foundation of Jiangsu Province of China (BK20161359); Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0604); The Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, China (UASP1503); The Science and Technology on Underwater Acoustic Antagonizing Laboratory, Systems Engineering Research Institute of CSSC(Grant No. MB80038) and Six Talent Peaks project of Jiangsu Province.

Author information

Authors and Affiliations

  1. School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Biao Wang, Feng Chen & Huilin Ge

  2. Zhenjiang Key Laboratory of Information Sensing and Communication of Smart Ocean, Zhenjiang, 212003, China

    Biao Wang

Authors
  1. Biao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huilin Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Chen, F. & Ge, H. Subspace projection semi-real-valued MVDR algorithm based on vector sensors array processing. Neural Comput & Applic 32, 173–181 (2020). https://doi.org/10.1007/s00521-018-3791-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3791-8

Keywords

Search

Navigation